Unapplied Cash: eIKON Dealer Compensation Joyce Darson NE Region CCC

2 Agenda n Project Overview n Baseline information n Analyzing the data n Recommendations n Next Steps n Questions/Open Discussion

3 Project Overview n We bill equipment leases and service contracts to IOSC n IOSC funds us weekly through the “dealer compensation report” n Frequent discrepancies between Oracle & IOSC systems n Result: n Cash will not automatically apply n Research, rework, added costs to apply cash at MSSC and CCC’s n Project and data are focused on eIKON environment only

4 Project Overview, continued n Baseline DPMO = 196,500 n Sigma Level =.85 n Defect = any dealer compensation cash that cannot be automatically applied

5 Project Status vs. Plan Project Milestones: Target DateStatus Define Phase08/22/03Complete Measure Phase09/19/03Complete Analyze Phase10/31/03On track Improve Phase11/28/03On track Control Phase12/31/03On track

6 Project Objectives & Benefits n Project Objective: - 70 % reduction in discrepancies between Oracle & CLAS (dealer compensation only). - Improve Sigma) to 2.3 n Project Benefits: - Estimated headcount savings of 12 and $440K per year from FY 2004 plan of 34; (reduction in required headcount for reconciliation and research). - Existing resources will be re-allocated to support future migrations. - Additional benefits in reduced rework/research at CCC’s (not quantified).

7 % Automatically Applied July- Sept: Average 80 % automatically applied Oracle conversion Avg.80 %

8 Process Capability (Baseline) As of 8/03, 100 % of process is outside target Target = 92 %

9 Process Issues Start End Process Issue & Backlog Process Flaw Need training Training issues & errors

10 Next… Analyzing the Results

11 Cause & Effect Matrix, FMEA n Used to determine top 8 – 10 Inputs into process n Identified as the following: n Employee Training n Funding amount n Lease information (rate, term, type) n “Closed” order n Oracle invoice # n Residual & buyout amounts n Next: examined each input for what could go wrong, and ranked issues according to frequency, impact, and causes (FMEA) n Gathered data on high value X’s from FMEA

12 FMEA & Data Collection Results Team input (“gut” feel) Actual Data !!

13 Regression Analysis If we can control the invoice #, we can control the unapplied cash

14 Other Analysis Used ANOVA method Found statistically significant difference between Atlanta and old CED/SWD Atlanta Avg 71 % CEDAvg 85 % SWDAvg 86 % More errors / discrepancies in Atlanta than Houston Better Results

15 Researched results, Atlanta vs. Houston n Detailed process review, each location n Process same in each area n Houston began doing audits August 2003 n No formal lease training available n Lack of tenure/experience in Order Coordination area n Number of processing centers for leases: n Atlanta: 11 locations n Houston: 4 locations n Less is better n Allows for more consistency in process

16 Recommendations n Process Changes: n Generate invoice after OC validation of funding n Fix errors found during validation prior to IOSC receiving deal n Address/eliminate backlog in credit/rebill area n Fewer locations for lease processing n IT Changes: n Automate upload of Oracle invoice # to CLAS system n Fix Oracle “glitch” causing multiple invoice #’s n Training: n No formal lease training n Too few “Subject Matter Experts” n Existing training: not process orientated

17 Next Steps n Continued data collection, based on information received 10/16/03 n Details of invoice number issues n % of deals requiring funding change after Oracle invoice generated n Due 10/27/03 n Complete detailed statistical analysis (10/31/03) n Begin Improve Phase (November)

18 Conclusion Questions & Discussion